Chandelier Exit

get_chandelier(quotes, lookback_periods=22, multiplier=3.0, chandelier_type=ChandelierType.LONG)

Parameters

name	type	notes
`quotes`	Iterable[Quote]	Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame
`lookback_periods`	int, default 22	Number of periods (`N`) for the lookback evaluation.
`multiplier`	float, default 3.0	Multiplier number must be a positive value.
`chandelier_type`	ChandelierType, default ChandelierType.LONG	Direction of exit. See ChandelierType options below.

Historical quotes requirements

You must have at least N+1 periods of quotes to cover the warmup periods.

quotes is an Iterable[Quote] collection of historical price quotes. It should have a consistent frequency (day, hour, minute, etc). See the Guide for more information.

ChandelierType options

from stock_indicators.indicators.common.enums import ChandelierType

type	description
`LONG`	Intended as stop loss value for long positions. (default)
`SHORT`	Intended as stop loss value for short positions.

Return

ChandelierResults[ChandelierResult]

This method returns a time series of all available indicator values for the quotes provided.
ChandelierResults is just a list of ChandelierResult.
It always returns the same number of elements as there are in the historical quotes.
It does not return a single incremental indicator value.
The first N periods will have None Chandelier values since there’s not enough data to calculate.

ChandelierResult

name	type	notes
`date`	datetime	Date
`chandelier_exit`	float, Optional	Exit line

Utilities

See Utilities and Helpers for more information.

Example

from stock_indicators import indicators
from stock_indicators import ChandelierType     # Short path, version >= 0.8.1

# This method is NOT a part of the library.
quotes = get_historical_quotes("SPY")

# calculate Chandelier(22,3)
results = indicators.get_chandelier(quotes, 22, 3, ChandelierType.LONG)

About Chandelier Exit

Created by Charles Le Beau, the Chandelier Exit is typically used for stop-loss and can be computed for both long or short types. [Discuss] 💬